Anti-inflammatory effects of dabrafenib in vitro and in vivo
Bibliographic record
Abstract
The screening of bioactive compound libraries can be an effective approach for repositioning FDA-approved drugs or discovering new treatments for human diseases (drug repositioning). Drug repositioning refers to the development of existing drugs for new indications. Dabrafenib (DAB) is a B-Raf inhibitor and initially used for the treatment of metastatic melanoma therapy. Here, we tested the possible use of DAB in the treatment of lipopolysaccharide (LPS)-mediated vascular inflammatory responses. The anti-inflammatory activities of DAB were determined by measuring permeability, neutrophils adhesion and migration, and activation of pro-inflammatory proteins in LPS-activated human umbilical vein endothelial cells (HUVECs) and mice. We found that DAB inhibited LPS-induced barrier disruption, expression of cell adhesion molecules (CAMs), and adhesion and transendothelial migration of neutrophils to human endothelial cells. DAB also suppressed LPS-induced hyperpermeability and leukocytes migration in vivo. Furthermore, DAB suppressed the production of tumor necrosis factor-α (TNF-α) or interleukin (IL)-6 and the activation of nuclear factor-κB (NF-κB) or extracellular regulated kinases (ERK) 1/2 by LPS. Moreover, treatment with DAB resulted in reduced LPS-induced lethal endotoxemia. These results suggest that DAB possesses anti-inflammatory functions by inhibiting hyperpermeability, expression of CAMs, and adhesion and migration of leukocytes, thereby endorsing its usefulness as a therapy for vascular inflammatory diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".